Perception of time to contact of slow- and fast-moving objects using monocular and binocular motion information

A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

Stable visually guided reaching does not require an internal feedforward model to compensate for internal delay: Data and model

Visually guided reaches are performed in ≈1s. Given unstable feedback control with neural transmission delay, stable visually guided reaching is assumed to require internal feedforward models that generate simulated feedback without delay that combines with actual feedback for stability. We investigated whether stable visually guided reaching requires internal models to handle such delay. Participants performed rapid targeted reaches in a virtual environment with different mappings between speeds of the hand and hand avatar. First, participants reached with visual guidance and constant mapping. Second, feedforward reaches were performed with constant mapping and hand avatar only visible at reach start and end. Reaches were accurate. Third, participants performed reaches with visual guidance and different mappings every trial. We expected performance as in the first condition. Finally, feedforward reaches with variable mapping yielded large errors showing visual guidance in the previous condition was successful despite an ineffective internal model. We simulated reaches using a proportional rate model with disparity Tau controlling the virtual Equilibrium Point in an Equilibrium Point (EP) model. The time dimensioned information and dynamic remained stable with delayed feedback. Finally, we fit movement times using the proportional rate EP model with 0msec, 50msec, and 100msec delay. With the fitted model parameters, we compared the model reach trajectories with the behavioral trajectories. Stable visually guided reaching did not require an internal feedforward model.

Auditory affective content facilitates time-to-contact estimation of visual affective targets

Reacting to a moving object requires an ability to estimate when a moving object reaches its destination, also referred to as the time-to-contact (TTC) estimation. Although the TTC estimation of threatening visually moving objects is known to be underestimated, the effect of the affective content of auditory information on visual TTC estimation remains unclear. We manipulated the velocity and presentation time to investigate the TTC of a threat or non-threat target with the addition of auditory information. In the task, a visual or an audiovisual target moved from right to left and disappeared behind an occluder. Participants' task was to estimate the TTC of the target, they needed to press a button when they thought that the target contacted a destination behind the occluder. Behaviorally, the additional auditory affective content facilitated TTC estimation; velocity was a more critical factor than presentation time in determining the audiovisual threat facilitation effect. Overall, the results indicate that exposure to auditory affective content can influence TTC estimation and that the effect of velocity on TTC estimation will provide more information than presentation time.

Time for Space and the Stability of Prospective Control: Reaching-to-Grasp Gibson

Gibson formulated an approach to goal-directed behavior using prospective information in the context of visually guided locomotion and manual behavior. The former was Gibson's paradigm case, but it is the rapidity of targeted reaching that has provided the special challenge for stable control. Recent treatments of visually guided reaching assume that internal forward models are required to generate stable behavior given delays caused by neural transmission times. Internal models are representations of the sort eschewed by Gibson in favor of prospective information. Reaching is usually described as guided using relative distances of hand and target, but prospective information is usually temporal rather than spatial. We describe proportional rate control models that incorporate time dimensioned prospective information and show they remain stable in the face of delays. The use of time-dimensioned prospective information removes the need for internal models for stable behavior despite neural transmission delays and allows Gibson's approach to prevail.